Object recognition, and in particular, human facial recognition, typically includes a computer application that is capable of identifying the identity of a person from a digital image or video.
Some object recognition algorithms identify objects in an image or video by extracting landmarks, or features, from the image or video. Such object recognition algorithms may be referred to as geometric algorithms. For example, geometric facial recognition algorithm may identify facial features by extracting landmarks/features from an image of a human face, and may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, jaw, lips, and/or other facial features of the human face in the image (the person or object in an image may be referred to as the “subject of the image” or “subject”, and the image representing the person or object to be identified may be referred to as a “subject image”). However, geometric algorithms typically require high resolution and/or low noise images in order to properly detect objects or facial features within the image. Additionally, geometric facial recognition algorithms usually require a complete picture of a subject in order to determine the relative position, size, and/or shape of the facial features of the subject. Furthermore, such algorithms typically require a relatively large set of sample images upon which to compare an object against in order to determine the identity of a subject. Moreover, geometric facial recognition algorithms may encounter errors when the subject image has different dimensions than the dimensions of a sample set of images due to translation (for example, scaled, rotated, skewed, and the like) of the subject image to fit the dimensions of sample images.
Humans' visual systems possess versatile and flexible tools to detect, recognize and identify objects in a dynamic real world environment and are capable of reasoning and extrapolating information effectively. However, human visual systems may face fatigue and be incapable of processing information at high speeds, may be very slow in partial or component processing, and may be unable to memorize many objects in a short time.